Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)–mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.


Participants
All of the subjects were individuals aged ≥ 65 who n.This study included 379 subjects who were invited to complete a comprehensive geriatric assessment and a face-to-face interview in the local community hospital.Our questionnaire assessed sociodemographic, lifestyle and health information.Sociodemographic variables included age and sex.Lifestyle includes smoking, drinking and daily activity levels.Daily activity levels were measured using the short form of the International Physical Activity Questionnaire (IPAQ) 18 .Health information included BMI, chronic conditions (such as diabetes, hypertension, hyperlipidemia, stroke, and heart disease, medication use and cognitive function.Cognitive function was assessed by the Mini-Mental State Examination (MMSE) 19 .Details of the questionnaire have been described in our previous study 20 .We excluded subjects who (1) did not complete the questionnaire (n = 8), (2) took antidepressants (n = 2) and ( 3) lacked blood samples (n = 1).Our subject screening process is shown in Fig. 1.The protocol of our study was reviewed and approved by the ethics committee at Shanghai University of Medicine and Health Sciences, China, and the methods were carried out in accordance with the principles of the Declaration of Helsinki.All the subjects provided informed consent before participation.

Metabolite identification and analysis
The LC-MS data were analysed using Proggenesis Qi software version 2.3 (Nonlinear, Dynamics, Newcastle, UK).First, the software is used to carry out meaningful data mining and perform advanced alignment, picking, normalization, and retention time (RT) correction.The obtained characteristic matrix includes information about the mass charge ratio (m/z), RT, and peak intensities.Then, the identification of metabolites was based on precise m/z, secondary fragments, and isotope distribution using the human metabolome database (HMDB), Human Metabolome Database (HMDB) (http:// www.hmdb.ca/), lipid maps (version 2.3) (http:// www.lipid maps.org/), METLIN (http:// metlin.scrip ps.edu/), and self-built databases (EMDB) for qualitative analysis.
The GC-MS data used the software MS-DIAL version 2.74 for peak detection, peak identification, characterization, peak alignment, wave filtering, etc. Metabolites were annotated through the LUG database (Untargeted database of GC-MS rom Lumingbio).The raw data matrix was obtained from the raw data with a three-dimensional dataset, including sample information, the name of the peak of each substance, retention time, retention index, mass-to-charge ratio, and signal intensity, after alignment with the Statistical Compare component.The internal standards with RSD > 0.3 were used to segment and normalize all peak signal intensities in each sample, and the segmented and normalized results were removed redundancy and merged peak to obtain the data matrix.
A total of 1008 compound identifications detected by LC-MS and 446 compound identifications detected by GC-MS were automatically linked to the compounds.Finally, orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to visualize the differences in metabolites between DC and NDC, and 200 response permutation tests (RPTs), including parameters such as R2 and Q2, were used to quantify the goodness of fit and assess the reliability of the established models.If these parameters were close to 1.0, the model was considered valid.Multidimensional coupling and single-dimensional analysis were used to select different metabolites between groups.The variable importance in projection (VIP) generated in OPLS-DA represented differential metabolites with biological significance.Furthermore, the significance of differential metabolites was further verified by Student's t test.Variables with VIP > 1.0 and p < 0.05 were considered to be differential metabolites.To quantify the diagnostic performance of differential metabolites, a receiver operating characteristic curve (ROC) analysis was carried out, and the value of the area under the ROC curve (AUC) was calculated.

Pathway analysis
To determine the mechanism of metabolic pathway variation, the differential metabolites were based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http:// www.kegg.jp/ kegg/ pathw ay.html) to carry out metabolic pathway enrichment analysis.Their KEGG ID and pathway were found, and then the number of metabolites enriched in the corresponding pathway was calculated.The pathway with a p < 0.05 was selected as an enriched pathway; its calculation formula is given as follows: where N is the total number of metabolites, n is the number of differential metabolites, M is the number of metabolites annotated as a specific pathway, and m is the number of differential metabolites annotated as a specific pathway.

Statistical analyses
Baseline sociodemographic and health-related characteristic analyses were performed using SPSS version 25.0 (SPSS Incorporation, Chicago, IL, USA), and p < 0.05 was regarded as statistically significant.Baseline sociodemographic and health-related characteristics were compared between the DC and the NDC using an independent t test for numeric variables and a chi-square test for categorical variables.Data with a normal distribution are expressed as the mean ± SD, and categorical variables are expressed as proportions.

Characteristics of the study population
According to the exclusion criteria, we excluded 11 subjects; the remaining 368 were included in the experiment.Of the 368 subjects we included in the experiment, 49 were diagnosed with depression according to the diagnostic criteria as DC.The 319 people without depression were matched with 49 people according to age and sex as NDC.As shown in Table 1, there was no significant difference in sociodemographic lifestyle and healthy conditions between the DC and the NDC (p > 0.05).GDS scores (p < 0.001) was significantly different between the two groups.

Untargeted GC/LC-MS of samples
A total of 446 compounds were identified in plasma via GC-MS, and 1012 were identified via LC-MS.To determine the difference in plasma metabolites between the two groups of samples, we used the OPLS-DA model.The OPLS-DA model showed that there was obvious separation and little overlap between the two groups (Fig. 2A,B).Two hundred permutation tests were confirmed to not be overfitted (Fig. 2C,D).

Potential biomarker analysis
Among all identified metabolites, 64 metabolites identified via GC-MS and 73 metabolites identified via by LC-MS had significant contributions to the differentiation between the DC and the NDC, with VIP values > 1 and p values < 0.05 (Tables 2, 3).Three substances with the same name and KEGG ID were detected by both methods, including hypoxanthine, phytosphingosine, and xanthine.The volcanic map shows the P value and fold change value, thus proving the effectiveness of differential metabolites (Fig. 3A,B).Hierarchical clustering displayed the levels of these metabolites, in which colours represent higher levels (red) or lower levels (blue), with the intensity reflecting the corresponding concentration (Fig. 3C,D).The top 10 metabolites are shown by box-and-whisker plots according to VIP values (Fig. 4).

Metabolic pathways change depression
To understand which metabolic pathways may affect depression, we conducted metabolic pathway enrichment (Fig. 5).We found that these metabolites are mostly related to purine metabolism and galactose metabolism.

Discussion
To our knowledge, our study is the first to use a nontargeted metabolomic method to study the plasma metabolic profile of depression in Chinese community-dwelling older adults.A total of 1458 metabolites were detected by LC-MS and GC-MS, including 137 different metabolites with VIP values > 1 and p values < 0.05.To identify reliable biomarkers, we made a volcano map, and we performed hierarchical clustering, box diagram analysis and ROC curve analysis for different metabolites.Furthermore, we also enriched the metabolic pathways and analysed the affected metabolic pathways.
In a previous study of major depressive disorder, 822 metabolites were detected in plasma using LC, and 17 metabolic pathway changes were found 22 .Thirty-seven metabolites were detected by GC-MS in the plasma of pregnant women with antenatal depressive symptoms 23 .Compared to using only LC-MS or GC-MS, we detected more metabolites using both LC-MS and GC-MS and discovered more differential metabolites and changes in metabolic pathways [12][13][14]22,23 . Previus studies have found changes in amino acid, fatty acid, and purine metabolism in plasma samples of depression, and our study also found similar findings.In addition, we Vol.:(0123456789) www.nature.com/scientificreports/also found changes in FoxO signaling and Ampk signaling pathway pathways, which are involved in cellular autophagy 24,25 .Therefore, our results suggest that depression may be related to cellular autophagy.
Our results revealed that 1-(sn-glycero-3-phospho)-1D-myo-inositol had a high diagnostic value.1-(sn-Glycero-3-phospho)-1D-myo-inositol, also known as glycerophosphoinositol, is produced through membrane phosphatidylinositol through two successive deacylation steps catalysed by phospholipase A2IVα 26,27 .According to a study, high glycerophosphoinositol levels indicate cellular phenomena associated with the activation of RAS/mitogen-activated protein kinase (MAPK) pathways 27 .Our metabolic pathway enrichment results showed that depression was closely associated with the MAPK signaling pathway.Ras/MAPK pathway alterations play a critical role in human brain structure and white matter microstructure 28 .In a study of depression in elderly individuals, it was found that white matter changes in elderly individuals with depression and that the changed white matter was related to cognitive control and emotional regulation 29 .Therefore, 1-(sn-glycero-3-phospho)-1D-myo-inositol may affect the structure of white matter through the Ras/MAPK pathway, leading to depression.
In this study, we found significant changes in several metabolic pathways, the most important of which was the purine metabolic pathway.Compared with the non-depression cohort, the depression cohort was characterized by higher levels of purine compounds (2′-deoxyguanosine 5′-monophosphate, 3′-AMP, adenosine monophosphate, xanthine, guanosine monophosphate, inosinic acid, adenine, and hypoxanthine) and lower levels of uric acid.Purine compounds are the substrate of purine metabolism, and uric acid is the end product of purine metabolism 30 .Thus, based on these findings, we suggest that downregulated purine metabolism may occur in older adults with depression.A previous study also showed that uric acid in the plasma of patients with depression decreased 30 .Uric acid has an important role in vivo as an antioxidant that provides more than 60% www.nature.com/scientificreports/antioxidant activity in plasma 31,32 .Depression is associated with increasing levels of oxidative stress 33 .Excessive oxidative stress leads to damage to the brain function of patients and various psychiatric symptoms.Downregulation of purine metabolism and lack of sufficient uric acid to fight oxidative stress result in brain damage and depression.However, purine metabolism was upregulated in a metabonomic study of children and adolescents with major depressive disorder 12 .Therefore, the role of purine metabolism in depression needs further study.
We have made certain achievements in metabolomics research on depression.Three substances (hypoxanthine, phytosphingosine, and xanthine) were detected by LC-MS and GC-MS.In addition, many articles also mentioned these metabolites 9,22,34 , so our results are reliable and repeatable.Hypoxanthine and xanthine affect the occurrence of depression through purine metabolism (described above).Phytosphingosine is classified as a sphingolipid, and the D-erythro-sphingosine that we detected is a sphingolipid 35 .There is a large amount of sphingolipids in the central nervous system.Their metabolites are an important structure of biological membranes and participate in many cell signal transduction pathways as second messengers 35,36 .Sphingolipids are acylated to produce ceramide 37 .A study injected ceramide into the hippocampus of mice, and then the proliferation, maturation, and survival of neurons in mice decreased, leading to depressive behaviour 38 .However, our results showed that the concentration of ceramide in plasma decreased in the depression cohort.This may be because ceramide enters the central nervous system through the blood-brain barrier and accumulates in the hippocampus, resulting in depression and a decrease in ceramide concentration in the periphery 39 .
However, our research still has some limitations.First, our sample size is small, including only elderly individuals aged 65 and above in Chongming, Shanghai.Secondly, our article only compared the metabolite differences between the two groups without an in-depth study of the metabolites.The Hamilton Depression Rating Scale is the most common tool for clinically diagnosing depression, but the GDC scale was used in this study.The two scales may have some differences in the diagnosis of depression.In future studies, we will increase the sample size and pay attention to the differences between the two scales for diagnosing depression to identify better biomarkers.At the same time, we will also conduct some in-depth studies on metabolites in subsequent studies, and we will also validate the metabolites in the depression model again.

Conclusion
In conclusion, our results suggest that there are several plasma metabolites associated with depression.Several of these metabolites have high diagnostic value and may be used as markers for depression diagnosis.Through further study of differential metabolites, we can also find changes in the metabolic pathway of depression.www.nature.com/scientificreports/

Figure 1 .
Figure 1.A flowchart of participant selection.

Figure 2 .
Figure 2. Multivariate date analysis of date from plasma between the depression cohert (DC) and nondepression corhort (NDC) base on GC/LC-MS.(a, c) OPLS-DA score plots (left panel) and statistical validation of the corresponding OPLS-DA model by permutation analysis (right panel) based on the GC-MS.(b, d) OPLS-DA score plots (left panel) and statistical validation of the corresponding OPLS-DA model by permutation analysis (right panel) based on the LC-MS.The two coordinate points are relatively far away on the score map, indicating that there is a significant difference between the two samples, and vice versa.The elliptical region represents a 95% confidence interval.

Figure 3 .Figure 4 .
Figure 3. Volcano plot and hierarchical clustering based on the LC/GC-MS of serum metabolites obtained from the depression cohert (DC) and non-depression corhort (NDC).(a) Volcano plot based on GC-MS.(b) Volcano plot based on LC-MS.(c) Hierarchical clustering based on GC-MS.(d) Hierarchical Clustering based on LC-MS.In (a, b), the blue dot represents metabolite with a downward trend, red represents metabolites with an upward trend, and the gray origin represents that the change of metabolites is not obvious.The area size of the point is related to the VIP value.In (c, d), the color from blue to red illustrates that metabolites Hexpression abundance is low to high in hierarchical clustering.

Figure 5 .
Figure 5. Metabolic pathway analysis based on the differentiated plasma metabolites.(a) Metabolic pathway analysis based on GC-MS.(b) Metabolic pathway analysis based on LC-MS.

Table 1 .
Baseline sociodemographic variables of the matched groups (N = 98).DC depression cohort, NDC non-depression cohort, BMI body mass index, IPAQ international physical activity questionnaire, HDL highdensity lipoprotein, LDL low-density lipoprotein, MMSE Mini-mental State Examination, GDS score Geriatric Depression Scale score.

Table 2 .
Differential metabolites detected by GC-MS.a Correlation coefficient and VIP value were obtained from OPLS-DA analysis.b p Value determined from Student's t-test.c Fold change between depression cohort and non-depression cohort.dRelative concentrations compared to non-depression cohort: ↑ = upregulated, ↓ = downregulated.FC fold change, VIP variable importance for projection.

Table 3 .
Differential metabolites detected by LC-MS.a Correlation coefficient and VIP value were obtained from OPLS-DA analysis.b p value determined from Student's t-test.c Fold change between depression cohort and non-depression cohort.dRelative concentrations compared to non-depression cohort: ↑ = upregulated, ↓ = downregulated.PC phosphatidylcholine, LysoPC lysophosphatidylcholine, PE phosphatidylethanolamine, TG triglyceride, Cer ceramide, FC fold change, VIP variable importance for projection.